Preparing autonomous vehicles for turns

ABSTRACT

The technology relates to maneuvering a vehicle prior to making a turn from a current lane of the vehicle. As an example, a route that includes making the turn from the lane is identified. An area of the lane prior to the turn having a lane width of at least a predetermined size is also identified. Sensor data identifying an object within the lane is received from a perception system of a vehicle. Characteristics of the object, including a location of the object relative to the vehicle, are identified from the sensor data. The vehicle is then maneuvered through the area prior to making the turn using the identified characteristics.

BACKGROUND

Autonomous vehicles, such as vehicles that do not require a humandriver, can be used to aid in the transport of passengers or items fromone location to another. Such vehicles may operate in a fully autonomousmode where passengers may provide some initial input, such as a pickupor destination location, and the vehicle maneuvers itself to thatlocation.

These vehicles are typically equipped with various types of sensors inorder to detect objects in the surroundings. For example, autonomousvehicles may include lasers, sonar, radar, cameras, and other deviceswhich scan and record data from the vehicle's surroundings. Sensor datafrom one or more of these devices may be used to detect objects andtheir respective characteristics (position, shape, heading, speed,etc.). These characteristics can be used to predict what an object islikely to do for some brief period into the future which can be used tocontrol the vehicle in order to avoid these objects. Thus, detection,identification, and prediction are critical functions for the safeoperation of autonomous vehicle.

In addition to the sensors, these vehicles may rely on highly detailedmaps of their environment. These maps are critical for both navigation,for instance determining how to get between two locations) as well aslocalization (determining where the vehicle is in the world). The mapsmay even include guide lines which the vehicle can follow in order tomaintain its position in a lane.

BRIEF SUMMARY

Aspects of the disclosure provide a method of maneuvering a vehicleprior to making a turn from a lane of a roadway. The method includesidentifying, by one or more processors, a route that includes making theturn from the lane; identifying, by the one or more processors, an areaof the lane prior to the turn having a lane width of at least apredetermined size; receiving, by the one or more processors, from aperception system, sensor data identifying an object within the lane;identifying, by the one or more processors, from the sensor data,characteristics of the object including a location of the objectrelative to the vehicle; and maneuvering, by the one or more processors,the vehicle through the area prior to making the turn using theidentified characteristics.

In one example, the predetermined size is of a sufficient distance tofit the object and the vehicle side by side. In another example, thesensor data further identifies a distance between a curb and a lane linewithin the area and identifying the area is based on the sensor data. Inanother example, maneuvering the vehicle through the area includes usingpre-stored map information corresponding to the area, the mapinformation includes information identifying the lane width, andidentifying the area is based on the map information.

In another example, method also includes, when the relative location ofthe object is in front of the vehicle, such that the object is closer tothe turn than the vehicle, determining whether the object is likely tomake the turn, and maneuvering the vehicle is further based on thedetermination. In this example, the determination is further based onwhether the sensor data indicates that a turn signal of the object isactivated. In addition or alternatively, the determination is furtherbased on whether the object is stopped behind a second object such thatthe object and the second object appear to be stacked and are likely toproceed to turn or not turn in a same manner. In this example, thedetermination is further based on whether the object is further to oneboundary of the area or another boundary of the area.

In another example, the method also includes, when the relative locationof the object is behind the vehicle, such that the vehicle is closer tothe turn than the object, determining whether the object is attemptingto pass the vehicle and make the turn, and maneuvering the vehicle isfurther based on the determination. In this example, the determinationis further based on the relative location of the object, and where theobject is further than the vehicle from the turn in a lateral direction,maneuvering the vehicle includes moving the vehicle towards the turn ata predetermined distance from the turn. In addition or alternatively,the determination is further based on the relative location of theobject, and where the object is closer than the vehicle from the turn ina lateral direction, maneuvering the vehicle includes allowing theobject to pass by the vehicle in the lane and thereafter moving thevehicle towards the turn.

Another aspect of the disclosure provides a system for maneuvering avehicle prior to making a turn from a lane of a roadway. The systemincludes one or more processors configured to: identify a route thatincludes making the turn from the lane; identify an area of the laneprior to the turn having a lane width of at least a predetermined size;receive, from a perception system, sensor data identifying an objectwithin the lane; identify, from the sensor data, characteristics of theobject including a location of the object relative to the vehicle; andmaneuver the vehicle through the area prior to making the turn using theidentified characteristics.

In one example, the predetermined size is of a sufficient distance tofit the object and the vehicle side by side. In another example, the oneor more processors are further configured to, when the relative locationof the object is in front of the vehicle, such that the object is closerto the turn than the vehicle, determine whether the object is likely tomake the turn, and maneuvering the vehicle is further based on thedetermination. In this example, the one or more processors are furtherconfigured to make the determination further based on whether the objectis stopped behind a second object such that the object and the secondobject appear to be stacked and are likely to proceed to turn or notturn in a same manner. In this example, the one or more processors arefurther configured to make the determination further based on whetherthe object is further to one boundary of the area or another boundary ofthe area.

In another example, the one or more processors are further configuredto, when the relative location of the object is behind the vehicle, suchthat the vehicle is closer to the turn than the object, determinewhether the object is attempting to pass the vehicle and make the turn,and maneuvering the vehicle is further based on the determination. Inthis example, the one or more processors are further configured to makethe determination further based on relative location of the object, andwhere the object is further than the vehicle from the turn in a lateraldirection, maneuvering the vehicle includes moving the vehicle towardsthe turn at a predetermined distance from the turn. In addition oralternatively, the one or more processors are further configured to makethe determination further based on the relative location of the object,and when the object is closer than the vehicle from the turn in alateral direction, maneuvering the vehicle includes allowing the objectto pass by the vehicle in the lane and thereafter moving the vehicletowards the turn. In another example, the system also includes thevehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withaspects of the disclosure.

FIG. 2 is an example diagram of map information in accordance withaspects of the disclosure.

FIGS. 3A-3D are example external views of a vehicle in accordance withaspects of the disclosure.

FIGS. 4-10 are example views of vehicles on a roadway corresponding tothe map information and data of FIG. 2 in accordance with aspects of thedisclosure.

FIG. 11 is an example view of vehicles on another roadway and data inaccordance with aspects of the disclosure.

FIG. 12 is an example view of vehicles on a further roadway and data inaccordance with aspects of the disclosure.

FIG. 13 is another example view of vehicles a roadway corresponding tothe map information and data of FIG. 2 in accordance with aspects of thedisclosure.

FIG. 14 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION

Overview

The technology relates to controlling a vehicle autonomously as thevehicle approaches a turn, for instance, a right turn at anintersection, parking lot, driveway, etc. Many lanes provide for a widerarea immediately before such turns, especially in suburban or urbanareas. In such cases, the lane may be wide enough to allow for a vehicleto move to the right, while another vehicle which will presumably turnleft or proceed “straight” across the intersection may move to the left,though there is no actual lane line delineating such areas.

When maneuvering between locations, these vehicles tend to follow apredetermined path which causes the vehicle to stay “centered” in thelane by maintaining a distance from a center or left side lane line orfollowing the aforementioned map guide lines. In this regard, a vehiclemay actually tend to stay to the left as a lane widens before a rightturn rather than move to the right at an appropriate time to make theright turn. However, this can be confusing to other vehicles (or theirdrivers) who may not understand based on such behavior that the vehicleis attempting to make a right turn. In many cases, using a turn signalmay not be sufficient to signal this intent.

Moreover, simply having the vehicle move to the right at a fixeddistance (in time or space) from the right turn can be dangerous,especially where there is a bicycle lane, parking spaces, or anothervehicle also attempting to move to the right. This is presumably becausethat other vehicle also is making a right turn at the intersection. Inthat regard, it critical that the vehicle assess its environment andoperate in a manner that signals intent of the vehicle to other roadusers, such as, for example, other vehicles, bicyclists, pedestrians.

In order to assess the vehicle's environment, the vehicle may beequipped with a perception system. The perception system may includevarious sensors which can detect and identify objects in the vehicle'senvironment. This sensor data may then be sent to the vehicle'scomputing devices for processing in real time.

In order to plan a route, the vehicle's computing devices may access mapinformation that describes an expected state of the vehicle'senvironment. Using this information, the vehicle's computing devices mayplan a route, for instance, which includes one or more turns or turningmaneuvers at an intersection, into a parking lot, driveway etc.

The vehicle's computing devices may then identify which (if any) of theturns correspond to “wide lane” areas proximate to those turns. In righthand drive countries, as with the examples depicted in the figures,these turns are likely to be right turns. In left hand drive countries,these turns are likely to be left turns. In this regard, the vehicle'scomputing devices may access the detailed map information, and suchareas may be flagged as wide lanes or identified based on whether thewidth of the lane in the map information meets a threshold distance.Alternatively, this information may be determined in real time based ondetected distances between lane lines or between a lane line and a curbreceived from the perception system.

Once a wide lane area prior to a right turn is identified as the vehicleapproaches the right turn, for instance from a predetermined distance intime or space, sensor data from the perception system may be analyzed toidentify a set of objects having certain characteristics which wouldindicate that such objects are relevant to the vehicle's behavior beforemaking the right turn. For instance, objects moving in the same lane asthe vehicle in front of or behind the vehicle may be identified. Inaddition, the set of identified objects may be filtered to remove anyobjects that are simply too far away from the vehicle.

For any objects remaining in the set, the vehicle's computing device maydetermine whether the objects are going to proceed through theintersection (no turns) and/or make a left hand turn or alternatively,make a right turn. To do so, the sensor data from the perception systemfor any of the set of identified objects may be further analyzed.

Using this information, as well as the relative location of the otherobject, when the vehicle is a predetermined distance in time or spacefrom making the right turn, the vehicle's computing devices maydetermine how to best maneuver the vehicle both to ensure the safety ofany passengers (and the other object) as well as to signal the intent tomake the right turn.

The features described herein allow a vehicle's computing devices tomaneuver the vehicle is a safe and practical way. For instance, bymaneuvering the vehicle as described above, the vehicle is able toposition itself in a way that signals its intent to move to the rightand complete a right turn which can be more effective than simply usinga turn signal. At the same time, by moving over only in certaincircumstances and allowing other vehicles to pass to the right of thevehicle where those other vehicles, the vehicle's computing devices maymaneuver the vehicle in a safer and more socially acceptable way.

Example Systems

As shown in FIG. 1, a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing devices 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing device-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “instructions” and “programs” may be used interchangeably herein.The instructions may be stored in object code format for directprocessing by the processor, or in any other computing device languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computing device registers, in a relational database asa table having a plurality of different fields and records, XMLdocuments or flat files. The data may also be formatted in any computingdevice-readable format.

The one or more processor 120 may be any conventional processors, suchas commercially available CPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor. Although FIG. 1 functionally illustrates theprocessor, memory, and other elements of computing devices 110 as beingwithin the same block, it will be understood by those of ordinary skillin the art that the processor, computing device, or memory may actuallyinclude multiple processors, computing devices, or memories that may ormay not be stored within the same physical housing. For example, memorymay be a hard drive or other storage media located in a housingdifferent from that of computing devices 110. Accordingly, references toa processor or computing device will be understood to include referencesto a collection of processors or computing devices or memories that mayor may not operate in parallel.

Computing devices 110 may include all of the components normally used inconnection with a computing device such as the processor and memorydescribed above as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen or any other electrical device that is operableto display information). In this example, the vehicle includes aninternal electronic display 152 as well as one or more speakers 154 toprovide information or audio visual experiences. In this regard,internal electronic display 152 may be located within a cabin of vehicle100 and may be used by computing devices 110 to provide information topassengers within the vehicle 100.

Computing devices 110 may also include one or more wireless networkconnections 156 to facilitate communication with other computingdevices, such as the client computing devices and server computingdevices described in detail below. The wireless network connections mayinclude short range communication protocols such as Bluetooth, Bluetoothlow energy (LE), cellular connections, as well as various configurationsand protocols including the Internet, World Wide Web, intranets, virtualprivate networks, wide area networks, local networks, private networksusing communication protocols proprietary to one or more companies,Ethernet, WiFi and HTTP, and various combinations of the foregoing.

In one example, computing devices 110 may be an autonomous drivingcomputing system incorporated into vehicle 100. The autonomous drivingcomputing system may capable of communicating with various components ofthe vehicle. For example, returning to FIG. 1, computing devices 110 maybe in communication with various systems of vehicle 100, such asdeceleration system 160, acceleration system 162, steering system 164,signaling system 166, routing system 168, positioning system 170,perception system 172, and power system 174 (for instance, a gas orelectric engine) in order to control the movement, speed, etc. ofvehicle 100 in accordance with the instructions 132 of memory 130.Again, although these systems are shown as external to computing devices110, in actuality, these systems may also be incorporated into computingdevices 110, again as an autonomous driving computing system forcontrolling vehicle 100.

As an example, computing devices 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computingdevices 110 in order to control the direction of vehicle 100. Forexample, if vehicle 100 is configured for use on a road, such as a caror truck, the steering system may include components to control theangle of wheels to turn the vehicle. Signaling system 166 may be used bycomputing devices 110 in order to signal the vehicle's intent to otherdrivers or vehicles, for example, by lighting turn signals or brakelights when needed.

Routing system 168 may be used by computing devices 110 in order todetermine and follow a route to a location. In this regard, the routingsystem 168 and/or data 134 may store detailed map information, e.g.,highly detailed maps identifying the shape and elevation of roadways,lane lines, lanes and heading information (identifying the headings ororientations of the lanes), intersections, crosswalks, speed limits,traffic signals, buildings, signs, real time traffic information,vegetation, or other such objects and information which is expected topersist. Thus, this information may be pre-stored, as opposed to beinggenerated by the perception system 172 in real time. In other words,this detailed map information may define the geometry of vehicle'sexpected environment including roadways as well as speed restrictions(legal speed limits) for those roadways. In addition, this mapinformation may include information regarding traffic controls, such astraffic signal lights, stop signs, yield signs, etc., which, inconjunction with real time information received from the perceptionsystem 172, can be used by the computing devices 110 to determine whichdirections of traffic have the right of way at a given location.

FIG. 2 is an example of map information for a section of roadway 200. Inthis regard, the map information includes information identifying theshape, location, and other characteristics of lanes 210, 211, 212, 213,214, 215, 216, 217, bounded by lane markers 220, 221, 222, 223, 224,225, 226, 227, 228, 229, 230, 231, bar lines 240, 241, 242, 243 whichidentify the beginning of an intersection, as well as intersection 250.

In some instances, the map information may even include informationabout how a vehicle should move through the map. For instance, FIG. 2also includes a set of rails 260, 261, 262, 263 for a vehicle travelingin lane 210 towards intersection 250. These rails may act as guidelinesfor the vehicle's computing devices in order to maintain appropriate andsafe position within a lane, through an intersection, etc. Returning toFIG. 2, by following different rails, the vehicle may make differentmaneuvers through the intersection 250, such as a left turn (byfollowing rail 260 to rail 261), proceeding through the intersection (byfollowing rail 260 to rail 262), or a right turn (by following rail 260to rail 263). Of course, the vehicle may deviate from these rails wherethere is another object in the way or other circumstance, such as aconstruction area, which would prevent the vehicle from safely followinga rail.

In this example, the map information also identifies or flags areas ofthe map immediately prior to an intersection (or parking lot into whichthe vehicle may turn) having particular characteristics. For instance,the map information may identify areas as “wide lanes.” In such cases,the characteristics of area may be such that the width of the lane inthe map information meets a threshold distance, for instance, a widthgreat enough for two vehicles, such as 16 feet or more or less, or whichwould be of a sufficient distance to fit an object (such as a detectedvehicle) and the vehicle 100 side by side within the area. As shown inFIG. 2, the map information identifies area 270 as a wide lane areawhich meets the threshold distance.

Although the map information is depicted herein as an image-based map,the map information need not be entirely image based (for example,raster). For example, the map information may include one or moreroadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features. Each featuremay be stored as graph data and may be associated with information suchas a geographic location and whether or not it is linked to otherrelated features, for example, a stop sign may be linked to a road andan intersection, etc. In some examples, the associated data may includegrid-based indices of a roadgraph to allow for efficient lookup ofcertain roadgraph features.

Positioning system 170 may be used by computing devices 110 in order todetermine the vehicle's relative or absolute position on a map or on theearth. For example, the position system 170 may include a GPS receiverto determine the device's latitude, longitude and/or altitude position.Other location systems such as laser-based localization systems,inertial-aided GPS, or camera-based localization may also be used toidentify the location of the vehicle. The location of the vehicle mayinclude an absolute geographical location, such as latitude, longitude,and altitude as well as relative location information, such as locationrelative to other cars immediately around it which can often bedetermined with less noise than absolute geographical location.

The positioning system 170 may also include other devices incommunication with computing devices 110, such as an accelerometer,gyroscope or another direction/speed detection device to determine thedirection and speed of the vehicle or changes thereto. By way of exampleonly, an acceleration device may determine its pitch, yaw or roll (orchanges thereto) relative to the direction of gravity or a planeperpendicular thereto. The device may also track increases or decreasesin speed and the direction of such changes. The device's provision oflocation and orientation data as set forth herein may be providedautomatically to the computing devices 110, other computing devices andcombinations of the foregoing.

The perception system 172 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic signals, signs, trees, etc. Forexample, the perception system 172 may include sensors such as LIDAR(lasers), sonar, radar, cameras and/or any other detection devices thatrecord data which may be processed by computing devices 110. In the casewhere the vehicle is a small passenger vehicle such as a car, the carmay include a laser or other sensors mounted on the roof or otherconvenient location. For instance, a vehicle's perception system may usethe various sensors to detect objects and their characteristics such aslocation, orientation, size, shape, type, direction and speed ofmovement, etc. The raw data from the sensors and/or the aforementionedcharacteristics can be quantified or arranged into a descriptivefunction or vector for processing by the computing devices 110. Asdiscussed in further detail below, computing devices 110 may use thepositioning system 170 to determine the vehicle's location andperception system 172 to detect and respond to objects when needed toreach the location safely.

FIGS. 3A-3D are examples of external views of vehicle 100. As can beseen, vehicle 100 includes many features of a typical vehicle such asheadlights 302, windshield 303, taillights/turn signal lights 304, rearwindshield 305, doors 306, side view mirrors 308, tires and wheels 310,and turn signal/parking lights 312. Headlights 302, taillights/turnsignal lights 304, and turn signal/parking lights 312 may be associatedwith the signaling system 166. Light bar 307 may also be associated withthe signaling system 166.

Vehicle 100 also includes sensors of the perception system 172. Forexample, housing 314 may include one or more laser devices having 360degree or narrower fields of view and one or more camera devices.Housings 316 and 318 may include, for example, one or more radar and/orsonar devices. The devices of the perception system 172 may also beincorporated into the typical vehicle components, such astaillights/turn signal lights 304 and/or side view mirrors 308. Each ofthese radar, camera, and lasers devices may be associated withprocessing components which process data from these devices as part ofthe perception system 172 and provide sensor data to the computingdevices 110.

The computing devices 110 may control the direction and speed of thevehicle by controlling various components. By way of example, computingdevices 110 may navigate the vehicle to a destination locationcompletely autonomously using data from the detailed map information,perception system 172, and routing system 168. In order to maneuver thevehicle, computing devices 110 may cause the vehicle to accelerate(e.g., by increasing fuel or other energy provided to the power system174 or engine by acceleration system 162), decelerate (e.g., bydecreasing the fuel supplied to the engine, changing gears, and/or byapplying brakes by deceleration system 160), change direction (e.g., byturning the front or rear wheels of vehicle 100 by steering system 164),and signal such changes (e.g., by lighting turn signals of signalingsystem 166). Thus, the acceleration system 162 and deceleration system160 may be a part of a drivetrain that includes various componentsbetween an engine of the vehicle and the wheels of the vehicle. Again,by controlling these systems, computing devices 110 may also control thedrivetrain of the vehicle in order to maneuver the vehicle autonomously.

Example Methods

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

Once a passenger or cargo, is safely in or has safely left the vehicle,the computing devices 110 may initiate the necessary systems to controlthe vehicle autonomously along a route to a destination location. Manyroutes may include turns at intersection or other locations (such asparking lots, etc.). In order to follow the route, the routing system168 may use the map information of data 134 to determine a path or routeto the destination location that follows a set of the rails of the mapinformation. The computing devices 110 may then maneuver the vehicleautonomously (or in an autonomous driving mode) as described above alongthe route towards the destination.

FIG. 4 depicts a section of roadway 400 corresponding to the section ofroadway 400 of the map information. For instance, the shape, location,and other characteristics of lanes 410, 411, 412, 413, 414, 415, 416,417, bounded by lane markers 420, 421, 422, 423, 424, 425, 426, 427,428, 429, 430, 431, bar lines 440, 441, 442, 443 which identify thebeginning of an intersection, as well as intersection 450 correspond tothe shape, location, and other characteristics of lanes 210, 211, 212,213, 214, 215, 216, 217, bounded by lane markers 220, 221, 222, 223,224, 225, 226, 227, 228, 229, 230, 231, bar lines 240, 241, 242, 243which identify the beginning of an intersection, as well as intersection250, respectively. In this example, vehicle 100 is traveling in lane 410towards an intersection 450 and is following a route that involvesfollowing rail 260 towards rail 263 in order to make a right turn atintersection 450. Of course, the rails represent data from the mapinformation and are not part of the section of roadway 400.

As noted above, the computing devices may maneuver the vehicle in orderto follow a predetermined path which causes the vehicle to stay“centered” in the lane by maintaining a distance from a center or leftside lane line or following the map guide lines or rails discussedabove. In this regard, as vehicle 100 approaches intersection 450 inlane 410, the computing devices 110 may control vehicle according to therails 260 and 263 in order to make a right turn at intersection 450.Because the computing devices 110 will cause the vehicle 100 to followthe rails, with only small deviations as discussed above for safety, thevehicle may actually tend to stay to the left as a lane widens before aright turn rather than move to the right at an appropriate time to makethe right turn. Again, this can be confusing to other vehicles (or theirdrivers) who may not understand based on such behavior that the vehicleis attempting to make a right turn. In many cases, using a turn signalmay not be sufficient to signal this intent.

The computing device 110 may thus also control the movements of thevehicle 100 in order to better signal “intent” of the computing devices.To do so, the vehicle's computing devices may then identify which (ifany) of the turns include the vehicle maneuvering through a wide lanearea proximate to those turns. In right hand drive countries, as withthe examples depicted in the figures, these turns are likely to be rightturns. In left hand drive countries, these turns are likely to be leftturns. In this regard, the computing devices 110 may access the detailedmap information corresponding to section of roadway 200 to identify anywide lane areas proximate to turns which the vehicle will make in orderto follow the route. Thus, using the example of FIG. 4, for the turn atintersection 250, the computing device 110 may identify area 270 of lane210 (shown in FIG. 2), corresponding to lane 410, immediately prior tointersection 250 and lane 217 at which the vehicle will make a turn inorder to follow the route. This may be repeated for all turns of theroute. Alternatively, wide lane areas may be determined by the computingdevices in real time based on information from the perception system172. For example by determining distances between detected lane lines orbetween a lane line and a curb identified in the sensor data, thecomputing devices may identify such wide lane areas.

Once a wide lane area prior to a turn is identified, as the vehicleapproaches the wide lane area and/or the turn, for instance from apredetermined distance in time or space, sensor data from the perceptionsystem 172 may be analyzed to identify a set of objects having certaincharacteristics. For example, the computing devices 110 may analyze thesensor data to identify characteristics of objects which would indicatethat such objects are relevant to the vehicle's behavior before makingthe right turn. Relevant object may include objects moving in the samelane as the vehicle in front of or behind the vehicle may be identified.In addition, the set of identified objects may be filtered to remove anyobjects that are simply too far away from the vehicle. For instance, ifan object is 8 seconds or 200 feet away, such an object may be discardedfrom the set.

For any objects remaining in the set, the vehicle's computing device maydetermine whether the objects are going to proceed through theintersection (no turn) and/or make a left turn at the intersection oralternatively, make a right turn at the intersection. To do so, thesensor data from the perception system for any of the set of identifiedobjects may be further analyzed. As an example, the sensor data may beanalyzed to identify whether the object is using a turn signal whichwould indicate a left or right turn.

Using this information, as well as the relative location of the otherobject, when the vehicle is the predetermined distance in time or spacefrom the intersection or where the vehicle will need to make the turn,the vehicle's computing devices may determine how to best maneuver thevehicle both to ensure the safety of any passengers (and the otherobject) as well as to signal the intent to make the right turn (inaddition to lighting a turn signal light to indicate the change inheading).

For instance, when the other object is in front of the vehicle andappears to be planning to make a right turn, such as, for example, wherethe other object is using a right turn signal, moving to the right, oris already stopped at an intersection, the vehicle's computing devicesmay maneuver the vehicle to move to the right immediately and to staybehind the other object. For instance, FIG. 5 corresponds to the exampleof FIG. 5 with the addition of vehicle 510 traveling in lane 410 towardsintersection 450. As shown, vehicle 510 is within the area 270 butremains closer to lane line 420 than lane line 421. As such, thecomputing devices may determine that as vehicle 510 appears to bestaying towards the right of lane 410, vehicle 510 is very likely tomake a right turn. In addition, other information of the sensor data,such as the identification of an illuminated or active right turnsignal, may further indicate that the vehicle 510 is very likely to makea right turn. Accordingly, the computing devices 110 may maneuver thevehicle 100 in order to follow arrow 520, thereby moving towards laneline 420, away from lane line 421, and directly behind vehicle 510,until the vehicle 100 reaches the intersection 450 and makes the turninto lane 417.

If the other object in front of the vehicle is staying in the center ortowards the left of the lane, the vehicle's computing devices maymaneuver the vehicle to move to the right immediately and to stay behindthe other object. For instance, FIG. 6 corresponds to the example ofFIG. 4 with the addition of vehicle 510 traveling in lane 410 towardsintersection 450. As shown, vehicle 610 is within the area 270 butremains closer to lane line 421 than lane line 420. As such, thecomputing devices 110 may determine that vehicle 610 appears to bestaying towards the left of lane 410. Accordingly, the computing devices110 may maneuver the vehicle 100 in order to follow arrow 620, therebymoving towards lane line 420 and away from lane line 421. In addition,given that vehicle 510 is in front of vehicle 100, in order to reducethe likelihood of a collision if vehicle 610 were to also move towardslane line 420, the computing devices 110 may maneuver vehicle 100 inorder to remain behind vehicle 510, until the vehicle 100 reaches theintersection 450 and makes the turn into lane 417.

Where the other object appears to be one of a series of “stacked”vehicles stopped at the intersection towards the left of the lane, thevehicle's computing devices may maneuver the vehicle to the right moreslowly, proceeding to the right of the other vehicle only if the othervehicle is stopped, moving very slowly, or not otherwise in the way. Forinstance, FIG. 7 corresponds to the example of FIG. 4 with the additionof vehicles 710 and 712 traveling in lane 410 towards intersection 450.As shown, vehicles 710 and 712 are within the area 270 but remain closerto lane line 421 than lane line 420. As such, the computing devices 110may determine that vehicles 710 and 712 appear to be staying towards theleft of lane 410 and arranged one behind the other in the aforementioned“stacked” configuration. Accordingly, the computing devices 110 maymaneuver the vehicle 100 in order to follow arrow 720, thereby movingtowards lane line 420 and away from lane line 421. In addition, giventhat vehicle 712 is in front of vehicle 100, in order to reduce thelikelihood of a collision if vehicle 610 were to also move towards laneline 420, the computing devices 110 may maneuver vehicle 100 in order toremain behind vehicle 712, until the vehicle 100 reaches theintersection 450 and makes the turn into lane 417.

If the vehicles are stopped at the intersection towards both the leftand right of the lane, the vehicle's computing devices may maneuver thevehicle to pull behind the vehicles to the right, but proceeding withcaution if the vehicle is passing any objects on the right. Forinstance, FIG. 8 corresponds to the example of FIG. 4 with the additionof vehicles 810, 812, and 814 traveling in lane 410 towards intersection450. As shown, vehicles 810 and 812 are within the area 270 but remaincloser to lane line 421 than lane line 420, while vehicle 814 is withinthe area 270 but remains closer to lane line 420 than lane line 421. Assuch, the computing devices 110 may determine that the vehicles 710 and720 appear to be staying towards the left of lane 410 and arranged onebehind the other in the aforementioned “stacked” configuration. Inaddition, vehicle 814 is positioned to the right of vehicle 810, and isthus likely to make a right turn.

This positioning of vehicles 810 and 814 may indicate that it is likelythat vehicle 810 and 814 will take different actions when entering theintersection 450. For example, the computing devices 110 may determinethat vehicle 810 may be likely to make a left turn (the likelihood ofwhich may be greater if the sensor data indicates that a left turnsignal of vehicle 810 is activated), while vehicle 814 may be likely tomake a right turn (the likelihood of which may be greater if the sensordata indicates that a right turn signal of vehicle 814 is activated).Alternatively, the computing devices 110 may determine that vehicle 810may be likely to make a left turn (the likelihood of which may begreater if the sensor data indicates that a left turn signal of vehicle810 is activated), while vehicle 814 may be likely to proceed to lane415 through the intersection (the likelihood of which may be greater ifthe sensor data indicates that no turn signal of vehicle is activated).In yet another alternative, the computing devices 110 may determine thatvehicle 810 may be likely to proceed through the intersection to lane415 (the likelihood of which may be greater if the sensor data indicatesthat no turn signal of vehicle 810 is activated), while vehicle 814 maybe likely to make a right turn (the likelihood of which may be greaterif the sensor data indicates that a right turn signal of vehicle 814 isactivated).

Accordingly, the computing devices 110 may maneuver the vehicle 100 inorder to follow arrow 820, thereby moving towards lane line 420 and awayfrom lane line 421. In addition, given that vehicle 814 is adjacent tovehicle 810, the computing devices 110 may maneuver vehicle 100 aroundvehicle 812 and immediately behind vehicle 814. Of course, in order toreduce the likelihood of a collision if vehicle 812 were to also movetowards lane line 420, the computing devices 110 may maneuver vehicle100 very cautiously around vehicle 812 an towards vehicle 814 until thevehicle 100 reaches the intersection 450 and makes the turn into lane417.

Where the other object is behind the vehicle, not only must the vehiclesignal its intent, but the vehicle's computing devices must be able todetermine the intent of the other object. For example, where the otherobject is directly behind the vehicle and/or slightly to the left, thevehicle's computing device may move the vehicle to the rightimmediately. For instance, FIG. 9 corresponds to the example of FIG. 4with vehicle 100 being slightly closer to intersection 450 and theaddition of vehicle 910 traveling in lane 410 towards intersection 450.As shown, vehicle 100 is within area 270 and vehicle 910 is justentering (or approaching) area 270. Accordingly, the computing devices110 may maneuver the vehicle 100 in order to follow arrow 920, therebymoving towards lane line 420 and away from lane line 421 relativelyquickly, or in reality, before vehicle 910 is able to maneuver itself tothe right of vehicle 100.

FIG. 10 corresponds to the example of FIG. 4 with vehicle 100 beingslightly closer to intersection 450 and the addition of vehicle 1010traveling in lane 410 towards intersection 450. As shown, vehicle 100 iswithin area 270 and vehicle 910 is at least partially within area 270.In this case, where the other object, here vehicle 1010, is slightly tothe right of vehicle 100 and is likely to pass on the right of vehicle100 in order to make a right turn. In such a situation, it may be thatthe other object is actually attempting to overtake the vehicle. In thissituation, rather than getting to the right immediately, as in theexample of FIG. 9, it may be safer for the computing devices 110 tomaintain vehicle 100's current speed or slowdown in order to allow theother object to pass, and then move to the right immediately after theother object has passed. In addition to the other object's relativelocation, the vehicle's computing devices may also consider the speed ofthe other object. If the speed is slower than the vehicle and there issufficient room to do so, the vehicle's computing devices may actuallymaneuver the vehicle to the right immediately. Other behaviors, such aswhether the other object is honking may also be considered as a factorto allow the other object to pass.

In addition, before moving to the right, the vehicle's computing devicesmay also consider whether there are any unoccupied parking spaces orbicycle lanes to the right. In such situations, the computing devices110, rather than pulling vehicle 100 completely to the right, may pullonly partially across a lane marker for parking spot or directlyadjacent the bicycle lane in order to signal the intent to move to theright, but to avoid passing into these areas.

For instance, FIG. 11 depicts vehicle 100 moving in a lane 1110 towardsan intersection 1120 in order to make a right turn into lane 1112. Inthis example, vehicle 100 is moving through a wide lane area 1130 thatmay be identified in the map information or in real time based on thesensor data. There are two parking spaces 1140 and 1142 (which may beidentified in the map information as well. In addition, a vehicle 1150is located behind vehicle 100. Accordingly, computing devices 110, maymaneuver vehicle 100 only partially across a part (for instance, anoutside edge 1144) of either one or both of parking spaces 1140 and1142, following arrow 1160 towards lane line 1114, in order to signalthe intent to move to the right, but to avoid passing into the parkingspot.

Similarly, FIG. 12 depicts vehicle 100 moving in a lane 1210 towards anintersection 1220 in order to make a right turn into lane 1212. In thisexample, vehicle 100 is moving through a wide lane area 1230 that may beidentified in the map information or in real time based on the sensordata. There is also a bicycle lane 1240 to the right of vehicle 100 inthe area which may be identified in the map information as well. Inaddition, a vehicle 1250 is located behind vehicle 100. Accordingly, thecomputing devices 110, may maneuver vehicle 100 only partially across anoutside edge 1242 of the bicycle lane 1240, following arrow 1260 towardslane line 1214 in order to signal the intent to move to the right, butto avoid passing into the parking spot.

In addition or alternatively, the data may be analyzed to determinewhether the object appears to be following another object. For instance,if a first object appears to be following the trajectory of a secondobject, such as where the trajectories follow the same general shape orpass through points within a short predetermined distance of oneanother, this may be a signal that the first object is lining up behindthe second object in order to proceed in the same manner. In otherwords, both objects are making a right turn or both proceeding straightthrough the intersection. Again, this may be used to determine how tomaneuver the vehicle prior to making the turn.

For instance, FIG. 13 corresponds to the example of FIG. 4 with theaddition of vehicles 1310 and 1312 traveling in lane 410 towardsintersection 450. As shown, vehicles 1310 and 1312 are within the area270 but are closer to lane line 421 than lane line 420. This mayindicate that vehicle 1312 is “lining” up behind vehicle 1312. In thisexample, the position of vehicle 1312 within area 270 proximate to laneline 420 may indicate that vehicle 1312 is likely to turn right intolane 417 at intersection 450. This likelihood may be increased if thesensor indicates that a right turn signal of vehicle 1312 is activated.In this example, vehicle 1310 appears to be following trajectory 1320which was traveled immediately before by vehicle 1312 in order to reachthe position of vehicle 1312 as shown in FIG. 13. As such, the computingdevices 110 may determine that vehicle 1312 is also likely to make aright turn into lane 417 at intersection 450. The computing devices 110may then use this information to maneuver vehicle 100. This likelihoodmay be increased if the sensor indicates that a right turn signal ofvehicle 1310 is activated. Thus, in the example of FIG. 13, thecomputing devices 110 may maneuver vehicle to also follow trajectory1320 or to move towards lane line 420 even faster than trajectory 1320would allow, though remaining at least a predetermined distance behindvehicle 1310 for safety.

Accordingly, the computing devices 110 may maneuver the vehicle 100 inorder to follow arrow 720, thereby moving towards lane line 420 and awayfrom lane line 421. In addition, given that vehicle 712 is in front ofvehicle 100, in order to reduce the likelihood of a collision if vehicle610 were to also move towards lane line 420, the computing devices 110may maneuver vehicle 100 in order to remain behind vehicle 722, untilthe vehicle 100 reaches the intersection 450 and makes the turn intolane 417.

Exact paths, distances, and/or points within a particular wide lane areato begin maneuvering the vehicle to the right may be identified usingmachine learning techniques and reviewing sensor data from prior rightturns from wide lanes. This may recording and examining how far and/orquickly other vehicles shifted to the right in similar situations at thesame or similar areas.

Although the examples above relate to right hand turns at intersections,such turns may also occur where the vehicle is entering a parking lot.In addition, the examples herein relate to right turns in right-handdrive countries, but would be equally as relevant to left turns inleft-hand drive countries.

FIG. 14 is an example flow diagram 1400 in accordance which may beperformed by one or more processors of one or more computing devices ofa vehicle, such as computing devices 110 of vehicle 100, in order tomaneuver a vehicle prior to making a turn. In this example, a route thatincludes making the turn from the lane is identified at block 1410. Anarea of the lane prior to the turn having a lane width of at least apredetermined size is identified at block 1420. Sensor data identifyingan object within the lane is received from a perception system of thevehicle at block 1430. Characteristics of the object including alocation of the object relative to the vehicle are identified from thesensor data at block 1440. The vehicle is maneuvered through the areaprior to making the turn using the identified characteristics at block1450.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

The invention claimed is:
 1. A method of maneuvering a vehicle in a laneof a roadway prior to making a turn from the lane, the methodcomprising: identifying, by one or more processors, a route thatincludes making the turn from the lane at an intersection; identifying,by the one or more processors, an area of the lane prior to the turn;determining, by the one or more processors, that the area of the laneprior to the turn has a lane width of at least a predetermined size,wherein the lane enables vehicles to both proceed across theintersection and to turn at the intersection; receiving, by the one ormore processors, from a perception system, sensor data identifying anobject within the lane, wherein the predetermined size of the lane widthof the area of the lane prior to the turn is of a sufficient distance tofit the object and the vehicle side by side in the same lane;identifying, by the one or more processors, from the sensor data,characteristics of the object including a location of the objectrelative to the vehicle; determining, by the one or more processors,from the sensor data, whether the object is likely to make the turn; andmaneuvering, by the one or more processors, the vehicle through the areaof the lane prior to making the turn based on the determination ofwhether the object is likely to make the turn and the identifiedcharacteristics.
 2. The method of claim 1, wherein the sensor datafurther identifies a distance between a curb and a lane line within thearea and wherein determining that the area of the lane prior to the turnhas the lane width of at least the predetermined size is based on thesensor data.
 3. The method of claim 1, wherein maneuvering the vehiclethrough the area includes using pre-stored map information correspondingto the area, the map information includes information identifying thelane width, and wherein identifying the area is based on the mapinformation.
 4. The method of claim 1, further including, when therelative location of the object is in front of the vehicle, such thatthe object is closer to the turn than the vehicle, determining whetherthe object is likely to make the turn, and wherein maneuvering thevehicle is further based on the determination of whether the object islikely to make the turn.
 5. The method of claim 4, wherein thedetermination of whether the object is likely to make the turn isfurther based on assessing whether the sensor data indicates that a turnsignal of the object is activated.
 6. The method of claim 4, wherein thedetermination of whether the object is likely to make the turn isfurther based on assessing whether the object is stopped behind a secondobject such that the object and the second object appear to be stackedand are likely to proceed to turn or not turn in a same manner.
 7. Themethod of claim 6, wherein the determination of whether the object islikely to make the turn is further based on assessing whether the objectis further to one boundary of the area or another boundary of the area.8. The method of claim 1, further including, when the relative locationof the object is behind the vehicle, such that the vehicle is closer tothe turn than the object, determining whether the object is attemptingto pass the vehicle and make the turn, and wherein maneuvering thevehicle is further based on the determination of whether the object isattempting to pass the vehicle and make the turn.
 9. The method of claim8, wherein the determination of whether the object is attempting to passthe vehicle and make the turn is further based on the relative locationof the object, and where the object is further than the vehicle from theturn in a lateral direction, maneuvering the vehicle includes moving thevehicle towards the turn at a predetermined distance from the turn. 10.The method of claim 8, wherein the determination of whether the objectis likely to make the turn is further based on the relative location ofthe object, and when the object is closer than the vehicle from the turnin a lateral direction, maneuvering the vehicle includes allowing theobject to pass by the vehicle in the lane and thereafter moving thevehicle towards the turn.
 11. The method of claim 1, wherein the area ofthe lane prior to the turn includes both a lane for vehicular trafficand a bicycle lane.
 12. A system for maneuvering a vehicle in a lane ofa roadway prior to making a turn from the lane, the system comprising:one or more processors configured to: identify a route that includesmaking the turn from the lane at an intersection; identify an area ofthe lane prior to the turn; determine that the area of the lane prior tothe turn has a lane width of at least a predetermined size, wherein thelane enables vehicles to both proceed across the intersection and toturn at the intersection; receive, from a perception system, sensor dataidentifying an object within the lane, wherein the predetermined size ofthe lane width of the area of the lane prior to the turn is of asufficient distance to fit the object and the vehicle side by side inthe same lane; identify, from the sensor data, characteristics of theobject including a location of the object relative to the vehicle;determine, by the one or more processors, from the sensor data, whetherthe object is likely to make the turn; and maneuver the vehicle throughthe area of the lane prior to making the turn based on the determinationof whether the object is likely to make the turn and the identifiedcharacteristics.
 13. The system of claim 12, wherein the one or moreprocessors are further configured to, when the relative location of theobject is in front of the vehicle, such that the object is closer to theturn than the vehicle, determine whether the object is likely to makethe turn, and wherein maneuvering the vehicle is further based on thedetermination of whether the object is likely to make the turn.
 14. Thesystem of claim 13, wherein the one or more processors are furtherconfigured to make the determination of whether the object is likely tomake the turn further based on assessing whether the object is stoppedbehind a second object such that the object and the second object appearto be stacked and are likely to proceed to turn or not turn in a samemanner.
 15. The system of claim 14, wherein the one or more processorsare further configured to make the determination of whether the objectis likely to make the turn further based on assessing whether the objectis further to one boundary of the area or another boundary of the area.16. The system of claim 12, wherein the one or more processors arefurther configured to, when the relative location of the object isbehind the vehicle, such that the vehicle is closer to the turn than theobject, determine whether the object is attempting to pass the vehicleand make the turn, and wherein maneuvering the vehicle is further basedon the determination of whether the object is attempting to pass thevehicle and make the turn.
 17. The system of claim 16, wherein the oneor more processors are further configured to make the determination ofwhether the object is attempting to pass the vehicle and make the turnfurther based on relative location of the object, and where the objectis further than the vehicle from the turn in a lateral direction,maneuvering the vehicle includes moving the vehicle towards the turn ata predetermined distance from the turn.
 18. The system of claim 16,wherein the one or more processors are further configured to make thedetermination of whether the object is likely to make the turn furtherbased on the relative location of the object, and when the object iscloser than the vehicle from the turn in a lateral direction,maneuvering the vehicle includes allowing the object to pass by thevehicle in the lane and thereafter moving the vehicle towards the turn.19. The system of claim 12 further comprising the vehicle.
 20. Thesystem of claim 12, wherein the area of the lane prior to the turnincludes both a lane for vehicular traffic and a bicycle lane.
 21. Thesystem of claim 12, wherein the sensor data further identifies adistance between a curb and a lane line within the area and wherein theone or more processors are further configured to make the determinationthat the area of the lane prior to the turn has the lane width of atleast the predetermined size based on the sensor data.